Citizen data science: An enterprise use case from inside the US intelligence community

Description

Each day, thousands of intelligence analysts are hard at work culling and correlating information from a multitude of repositories with fine-grained security. The US Department of Defense (DoD) and the greater intelligence community (IC) face a challenge: How can they empower this community—the majority of whom are not steeped in traditional technical disciplines like software engineering or statistics—to translate their tradecraft into code and gain efficiencies in their workflow?

While the classified environment and mission of the DoD and IC may be unique, they face many common challenges in building grassroots traction of new technologies. Dave Stuart explains how Jupyter was used inside the DoD and IC to empower thousands of “citizen data scientists” to build and share analytics in order to meet the community’s dynamic challenges.

Driving a culture change such as the enterprise-wide adoption of Jupyter fed by organic growth means addressing a variety of challenges, such as closing the gap between data scientists and business (intel) analysts, bringing new users with little or no technical background into the Jupyter community, crowdsourcing solution development by empowering users to create their own solutions, automatically measuring the impact and health of notebooks created across a diverse user base, maximizing the “discoverability” of notebooks to enable users to quickly find relevant analytics for their mission, and securing buy-in from a variety of stakeholders within a large enterprise. Dave details how one very small team evangelized Jupyter across a large and diverse community and built an application called nbgallery (aka Notebook Gallery) that enables the citizen data science community to manage, share, and collaborate on code.